Debt maturity structure of private lodging firms

This study tests the signalling and liquidity risk hypotheses about the choice of debt maturity structure in the context of private lodging firms. We find a positive and significant relationship between the Z-score (as a proxy for firm quality) and the proportion of short-term debt. This finding supports the signalling hypothesis that low-quality firms prefer to issue long-term debt, while high-quality firms issue short-term debt. However, we find no evidence consistent with the liquidity risk hypothesis. We further find that the most influential factor affecting debt maturity is firm size, followed by Z-scores and then firm age. Our findings are robust to alternative estimation specifications and endogeneity concerns. Overall, our study contributes to expanding our understanding of the debt maturity structure in private firms.

Keywords: Debt maturity structure; private firms; lodging industry; signalling hypothesis; liquidity risk hypothesis

I. Introduction

Corporate debt maturity structure is the outcome of financial decision-making that can affect firm value. While prior studies investigating the debt maturity choice of public firms are abundant (Barclay and Smith [3]; Guedes and Opler [14]; Stohs and Mauer [22]; Datta, Iskandar-Datta, and Raman [9]; Brockman, Martin, and Unlu [7]; Ben-Nasr, Boubaker, and Rouatbi [5]; Belkhir, Ben-Nasr, and Boubaker [4]; Huang, Tan, and Faff [15]; Boubaker et al. [6]; Vijayakumaran and Vijayakumaran [23]), evidence of the debt maturity choice of private firms is relatively scarce (Díaz-Díaz, García-Teruel, and Martínez-Solano [11]; Orman and Köksal [18]). This study is motivated by the lack of research using a sample of private firms. The finance literature has proposed several hypotheses to explain the debt maturity structure choice. Among the hypotheses, we specifically test the signalling and liquidity risk hypotheses of debt maturity based on private firms in a specific industry, i.e. the lodging industry. The signalling hypothesis suggests that low-quality firms issue long-term debt with relatively lower refinancing costs, while high-quality firms issue short-term debt to signal their 'good type' regardless of high refinancing costs (Flannery [13]). The liquidity risk hypothesis proposed by Diamond ([10]) predicts that low-quality firms issue long-term debt due to liquidity risk, while high-quality firms issue short-term debt. The hypothesis supposes that very low-quality firms are compelled to issue short-term debt as they are screened out of the long‐term debt market. Diamond's ([10]) proposition implies that there is a nonlinear relationship between firm quality and debt maturity.

We focus on unlisted Korean lodging firms because they provide a unique setting for our study. The Korean lodging industry has grown rapidly over the last decade. Despite the significant impact of the continuous growth of the lodging industry on the real economy, its corporate financing policies, such as debt maturity structure choice, have yet to be explored in depth. The lodging industry has distinct characteristics from other sectors. For instance, the lodging industry tends to invest in a higher level of fixed assets, and its cash flows from the investment are realized over the long term. Thus, the lodging industry may minimize its liquidity risk by increasing the proportion of non-current liabilities to total equity and liabilities. However, given the current Korean situation where long-term bond markets are relatively less developed than the U.S. and U.K. and private firms have difficulty in issuing long-term bonds, the proportion of short-term debt (or current liabilities) for private lodging firms is expected to be high. For example, our sample of private Korean lodging firms indicates that the proportion of short-term debt to total debt, on average, exceeds 50%. As the proportion of short-term debt becomes excessively higher, firms are more exposed to fluctuation in interest rates because their debt maturity structure is concentrated at specific points in time. In the worst scenario, there may be a higher likelihood of structural liquidity problems due to rollover or refinancing risk. Therefore, it is an empirical question of whether existing hypotheses better describe the debt maturity structure of private lodging firms.

Using a sample of private Korean lodging firms between 2003 and 2016, we test the debt maturity structure hypotheses. Consistent with the signalling hypothesis, we find a positive and significant relationship between the Z-score (as a proxy for firm quality) and the proportion of short-term debt. The finding indicates that high-quality firms tend to issue short-term debt. To test the liquidity risk hypothesis, we repeat our analysis by further including the square of Z-score in the regression equation. We find no evidence for supporting the liquidity risk hypothesis. In particular, we calculate standardized coefficients for statistically significant variables. We find that firm size is the most important factor that affects debt maturity structure, and then is followed by Z-score and firm age. Our empirical findings are robust to alternative estimation specifications and endogeneity issues.

Our study contributes to the growing literature on the debt maturity structure of private firms. Although a number of prior studies have tested the hypotheses of debt maturity using firms in the U.S., Europe, and Asia, they primarily focus on a sample of public firms (or listed firms), especially non-financial or manufacturing firms. Unlike public firms, private firms are highly dependent on debt financing from banks; that is, they have a close relationship with banks, which makes a difference in the choice of debt maturity relative to public firms (Cole [8]). Private firms also have limited access to long-term debt financing due to their lack of transparency (Diamond [10]; Díaz-Díaz, García-Teruel, and Martínez-Solano [11]). Although previous studies have attempted to test debt maturity hypotheses based on public or private firms, they provide inconsistent findings that will be further explored. Evidence from this study can help creditors and regulators better understand the financing decision of private firms. Thus, this study fills a gap in the literature and expands our understanding of debt maturity structure choice by focusing on private firms.

Our study also contributes to the nascent literature on financing decisions of lodging firms. We have a limited understanding of the choice of debt maturity structure in the lodging industry, which is distinct from other sectors. The signalling and liquidity risk hypotheses may be challenging to apply to private lodging firms. If high-quality private lodging firms generate continuous cash flows, they may issue short-term debt to avoid the mispricing of long-term debt. However, the mispricing of long-term debt in private lodging firms may not be severe because they primarily obtain debt financing through financial institutions rather than bond issuance. Thus, high-quality firms may prefer not to issue short-term debt with high refinancing costs. Therefore, we extend the literature by testing the signalling and liquidity risk hypotheses using a sample of unlisted lodging firms.

The remainder of this paper is organized as follows. Section II discusses the sample selection and research design. Section III presents the results. Section IV concludes the study.

II. Sample and research design

Sample selection

We obtain annual financial data from the KIS-Value database, which is provided by NICE Information Service, between 2003 and 2016. We then collect a sample of private lodging firms based on two-digit Korea Standard Industry Classification (KSIC) codes. We remove firm-year observations with missing values. We winsorize all continuous variables at the 1% and 99% levels to minimize the impact of extreme outliers. Our final sample contains 1,801 firm-year observations and represents 228 firms.

Empirical model

For our empirical analysis, we estimate the following baseline model:

(1)

Graph

$\begin{array}{rl}& Short\mathrm{\_}Term\mathrm{\_}Deb{t}_{i,t}={\beta }_0+{\beta }_{1}Z\mathrm{\_}Scor{e}_{i,t}\\ & +{\beta }_{2}Asset\mathrm{\_}Maturit{y}_{i,t}\\ & +{\beta }_{3}Firm\mathrm{\_}Siz{e}_{i,t}+{\beta }_{4}Asset\mathrm{\_}Growt{h}_{i,t}\\ & +{\beta }_{5}Earnings\mathrm{\_}Volatilit{y}_{i,t}+{\beta }_{6}Effectve\mathrm{\_}Ta{x}_{i,t}\\ & +{\beta }_{7}Firm\mathrm{\_}Ag{e}_{i,t}+{\beta }_{8}Leverag{e}_{i,t}\\ & +{\mathrm{\Sigma }}_{i,t}Year\mathrm{\_}Dummy+{{\epsilon }_{i,t}}_{,}\end{array}$

where subscripts i and t refer to the firm and year, respectively. We use the proportion of debt maturing in 1 year or less (Short_Term_Debt) as a proxy for debt maturity structure. For testing a signalling hypothesis, as in Brockman, Martin, and Unlu ([7]) and Huang, Tan, and Faff ([15]), we use the Altman's ([1]) Z-score (Z_Score) as a proxy for firm quality.[1] The hypothesis predicts that low-quality firms prefer to issue long-term debt due to its relatively low refinancing costs, while high-quality firms issue short-term debt to signal their excellence. High Z-scores indicate that firms have a lower possibility of financial default (i.e., high-quality firms). If the signalling hypothesis holds, then Z_Score is expected to be positively related to Short_Term_Debt.

Diamond's ([10]) liquidity risk hypothesis suggests that high-quality or extremely low-quality firms issue short-term debt, while firms with other levels of quality issue long-term debt, indicating a nonlinear association between firm quality and debt maturity. To test the hypothesis, we follow Scherr and Hulburt ([19]) and use the Z-score (Z_Score) and the square of the Z-score (Z_Score2). If the liquidity risk hypothesis holds, then it is expected that the Z-score is negatively related to the proportion of short-term debt, while the square of the Z-score is positively related to the debt maturity.

We control for asset maturity (Asset_Maturity) as Stohs and Mauer ([22]) find that firms with longer asset maturity issue less short-term debt. We control for firm size (Firm_Size) as larger firms issue more long-term debt because they can easily access to capital markets and accomplish the economies of scale (Smith and Warner [21]). Following Scherr and Hulburt ([19]), we control for asset growth rates (Asset_Growth). We control for earnings volatility (Earnings_Volatility) as firms with greater volatility may be related to lower tax rates and issue debt with shorter maturity (Scholes and Wolfson [20]). We control for effective tax rates (Effectve_Tax) as firms with higher tax rates issue more long-term debt because they can take advantage of tax deduction from using debt (Kane, Marcus, and McDonald [16]). Flannery ([13]) argues that firms with high information asymmetry issue short-term debt to minimize refinancing costs. Thus, we control for firm age (Firm_Age) as younger firms may have higher information asymmetry and issue short-term debt. We control for leverage (Leverage) as Stohs and Mauer ([22]) find that firms with higher leverage issue more long-term debt. In equation (1), we further include year fixed effects to control for macroeconomic conditions that can affect debt maturity. All variables are defined in Appendix A.

III. Results

Descriptive statistics and Pearson correlations

Table 1 shows the descriptive statistics for relevant variables. The mean and median values of Short_Term_Debt are 0.506 and 0.495, respectively.

Table 1. Descriptive statistics

Variable	N	Mean	Std. Dev.	Min.	25th Pctl.	Median	75th Pctl.	Max.
Short_Term_Debt	1801	0.506	0.306	0.015	0.234	0.495	0.787	1.000
Z_Score	1801	1.102	1.591	−0.346	0.294	0.627	1.309	10.136
Asset_Maturity	1801	33.768	35.401	0.355	15.962	26.025	39.844	260.597
Firm_Size	1801	24.564	1.083	23.231	23.589	24.329	25.329	26.490
Asset_Growth	1801	0.061	0.220	−0.285	−0.025	−0.001	0.057	1.268
Earnings_Volatility	1801	0.019	0.019	0.001	0.007	0.013	0.025	0.105
Effectve_Tax	1801	0.111	0.167	−0.302	0.000	0.000	0.219	0.762
Firm_Age	1801	2.828	0.736	1.099	2.303	2.944	3.434	4.025
Leverage	1801	0.605	0.254	0.040	0.417	0.631	0.824	0.991

1 This table reports descriptive statistics for variables used in our study. We use a sample of 1,801 firm-year observations between 2003 and 2016. All variables are defined in Appendix A.

Table 2 presents the Pearson correlation coefficients among the variables. These correlations suggest that firms with better financial conditions, smaller size, and higher earnings volatility and older firms are significantly correlated with shorter debt maturity structure.

Table 2. Pearson correlation coefficients

Variable	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
(1) Short_Term_Debt	1.00
(2) Z_Score	0.15	1.00
(3) Z_Score2	0.15	0.92	1.00
(4) Asset_Maturity	−0.05	−0.11	−0.02	1.00
(5) Firm_Size	−0.26	0.07	0.02	−0.04	1.00
(6) Asset_Growth	−0.03	−0.06	−0.06	0.25	0.06	1.00
(7) Earnings_Volatility	0.09	0.06	0.02	−0.15	−0.25	0.12	1.00
(8) Effectve_Tax	0.03	0.18	0.04	−0.11	0.05	−0.03	−0.03	1.00
(9) Firm_Age	0.07	0.21	0.13	−0.09	0.16	−0.04	−0.06	0.14	1.00
(10) Leverage	−0.03	−0.67	−0.46	0.07	−0.28	0.02	0.05	−0.17	−0.26	1.00

2 This table shows Pearson correlation coefficients between variables. We use a sample of 1,801 firm-year observations between 2003 and 2016. Correlations in bold denote the statistical significance at the 1% level. All variables are defined in Appendix A.

Main regression results 2

Table 3 shows the results of pooled ordinary least‐squares (OLS) regressions. Column (1) shows that the coefficient on Z_Score is positive and significant. This finding indicates that firms with a lower possibility of default risk choose shorter maturity debt, supporting the signalling hypothesis. The coefficient on Asset_Maturity is negative but insignificant, while the coefficient on Firm_Size is negative and significant. The coefficients on Asset_Growth and Earnings_Volatility are positive but insignificant. We find that the coefficient on Firm_Age is positive and significant. The coefficients on Effectve_Tax and Leverage are positive and insignificant.

Table 3. Testing the signalling and liquidity risk hypotheses

	Dependent variable: Short_Term_Debt
Independent variables	Baseline model (1)	Standardized coefficients (2)	Rank (3)	Testing the liquidity risk hypothesis (4)
Intercept	2.312*** (12.90)			2.299*** (12.44)
Z_Score	0.034*** (5.78)	0.178	2	0.040** (2.39)
Z_Score2				−0.001 (−0.38)
Asset_Maturity	−0.000 (−1.330)			−0.000 (−1.29)
Firm_Size	−0.079*** (−12.09)	−0.280	1	−0.079*** (−11.97)
Asset_Growth	0.018 (0.56)			0.018 (0.55)
Earnings_Volatility	0.077 (0.18)			0.051 (0.12)
Effectve_Tax	0.002 (0.04)			−0.003 (−0.08)
Firm_Age	0.035*** (3.47)	0.085	3	0.035*** (3.42)
Leverage	0.046 (1.17)			0.056 (1.18)
Year fixed effects	Yes			Yes
Adj. R2	0.101			0.100
N	1801			1801

3 This table shows the results of pooled OLS regressions. We use a sample of 1,801 firm-year observations between 2003 and 2016. We use robust standard errors clustered at the firm level. The t-statistics are in parentheses. ***, **, * denote the statistical significance at the 1%, 5%, and 10% levels, respectively. All variables are defined in Appendix A.

In columns (2) and (3), we find that firm size is the most crucial factor that affects debt maturity choice, followed by Z-score and firm age.

In column (4), we test the liquidity risk hypothesis of debt maturity. The coefficient on Z_Score is positive and significant, while the coefficient on Z_Score2 is negative but insignificant. These findings are not consistent with the liquidity risk hypothesis proposed by Diamond ([10]). These inconsistent results are probably because private Korean lodging firms may face less liquidity constraints due to their close ties with banks. Thus, even low-quality firms may issue short-term debt.

Alternative estimation specifications and endogeneity issues

We repeat our analysis using alternative estimation methods, such as Fama and MacBeth ([12]) cross-sectional regressions, firm fixed effects regressions, and a dynamic panel generalized method of moments (GMM) estimator. In particular, the dynamic panel GMM estimation proposed by Arellano and Bond ([2]) can be effective in mitigating endogeneity concerns.

Table 4 reports the results from three alternative estimation methods. We find a positive and significant coefficient on Z_Score, suggesting that firms with lower default risk choose a higher proportion of short-term debt. This finding supports the signalling hypothesis of debt maturity.

Table 4. Three alternative estimation specifications and endogeneity issues

	Dependent variable: Short_Term_Debt
Independent variables	Fama and MacBeth (1973) cross-sectional (1)	Firm fixed effects (2)	GMM (3)
Intercept	2.789*** (6.01)	3.052*** (5.23)	4.515 (1.19)
Lagged Short_Term_Debt			0.711*** (8.42)
Z_Score	0.038*** (8.78)	0.021* (1.68)	0.049* (1.76)
Asset_Maturity	−0.001 (−1.77)	−0.000 (−0.94)	0.000 (0.11)
Firm_Size	−0.097*** (−5.98)	−0.101*** (−4.41)	−0.188 (−1.21)
Asset_Growth	0.007 (0.22)	−0.006 (−0.22)	−0.005 (−0.02)
Earnings_Volatility	−0.569 (−0.92)	−0.309 (−0.76)	−1.122 (−1.33)
Effectve_Tax	0.003 (0.05)	0.003 (0.12)	−0.182 (−1.54)
Firm_Age	0.027*** (4.23)	0.037 (0.90)	0.077 (1.13)
Leverage	0.026 (0.47)	−0.252*** (−4.15)	0.032 (0.16)
Year fixed effects	No	Yes	Yes
Firm fixed effects	No	Yes	No
Adj. R2	0.188	0.637
N	1801	1801	1554
Arellano-Bond test for AR(1)	p-value = 0.00
Arellano-Bond test for AR(2)	p-value = 0.93
Sargan-Hansen test of the overidentifying restrictions	p-value = 0.51

4 This table shows the results of three alternative estimation methods. For the Fama and MacBeth ([12]) cross-sectional regression, we use Newey and West ([17]) standard errors to correct the heteroscedasticity. Regarding the firm fixed effects regression and GMM estimator, we use robust standard errors clustered at the firm level. The t-statistics are in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. All variables are defined in Appendix A.

We also repeat our analysis after including the square of the Z-score in Table 4. In unreported results, we find no evidence supporting the liquidity risk hypothesis.

Taken together, these findings suggest that our main results are robust to alternative estimation specifications and endogeneity issues.[3]

IV. Conclusion

This study tests the signalling and liquidity risk hypotheses of debt maturity using a sample of private lodging firms. We find that the signalling hypothesis can serve as a useful rationale for understanding the debt maturity choice of the firms. We also find that the most influential determinant of debt maturity is firm size, followed by Z-scores and then firm age. We find no evidence supporting the liquidity risk hypothesis. Our findings are robust to alternative estimation methods and endogeneity problems. Overall, our study sheds light on the debt maturity structure choice in private firms.

Acknowledgment

We are grateful for helpful comments from two anonymous referees and Mark P. Taylor (the editor).

Disclosure statement

No potential conflict of interest was reported by the author.

Appendix A. Variable definitions

Variable	Predicted signs in Equation (1)	Definition
Short_Term_Debt		Debt maturing in 1 year or less divided by total debt.
Z_Score	+	The Altman's Z-score is calculated as follows: Z = 0.717×(working capital/total assets) + 0.847×(retained earnings/total assets) + 3.107× (earnings before interest and taxes/total assets) + 0.420×(book value of equity/book value of total liabilities) + 0.998×(sales/total assets).
Asset_Maturity	–	Weighted-average maturities of gross property, plant, and equipment (PP&E) and current assets. Asset maturity is calculated as: (maturity of gross property, plant, and equipment (PP&E)) × (PP&E/total assets) + (maturity of current assets) × (current assets/total assets). The maturity of PP&E is defined as the tangible assets minus assets with construction in progress (CIP), divided by depreciation expenses. The maturity of current assets is defined as the current assets divided by operating expenses.
Firm_Size	–	Natural logarithm of total assets.
Asset_Growth	+	Percentage changes in total assets from year t-1 to t.
Earnings_Volatility	+	Standard deviation of return on assets (i.e., earnings/total assets) over the previous three years.
Effectve_Tax	–	Total tax expenses divided by pre-tax income.
Firm_Age	–	Natural logarithm of the number of years since a firm's establishment.
Leverage	–	Total debt divided by total assets.

1 Altman, E. I. 1977. The Z-score Bankruptcy Model: Past, Present and Future, Financial Crises. New York, NY : Wiley-Interscience. 2 Arellano, M., and S. Bond. 1991. " Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations." The Review of Economic Studies 58 : 277 – 297. doi: 10.2307/2297968. 3 Barclay, M. J., and C. W. Smith. 1995. " The Maturity Structure of Corporate Debt." The Journal of Finance 50 : 609 – 631. doi: 10.1111/j.1540-6261.1995.tb04797.x. 4 Belkhir, M., H. Ben-Nasr, and S. Boubaker. 2016. " Labor Protection and Corporate Debt Maturity: International Evidence." International Review of Financial Analysis 45 : 134 – 149. doi: 10.1016/j.irfa.2016.01.012. 5 Ben-Nasr, H., S. Boubaker, and W. Rouatbi. 2015. " Ownership Structure, Control Contestability, and Corporate Debt Maturity." Journal of Corporate Finance 35 : 265 – 285. doi: 10.1016/j.jcorpfin.2015.10.001. 6 Boubaker, S., L. Chourou, M. Haddar, and T. Hamza. 2019. " Does Employee Welfare Affect Corporate Debt Maturity?." European Management Journal 37 : 674 – 686. doi: 10.1016/j.emj.2019.08.004. 7 Brockman, P., X. Martin, and E. Unlu. 2010. " Executive Compensation and the Maturity Structure of Corporate Debt." The Journal of Finance 65 : 1123 – 1161. doi: 10.1111/j.1540-6261.2010.01563.x. 8 Cole, R. A. 2013. " What Do We Know about the Capital Structure of Privately Held US Firms? Evidence from the Surveys of Small Business Finance." Financial Management 42 : 777 – 813. doi: 10.1111/fima.12015. 9 Datta, S., M. Iskandar-Datta, and K. Raman. 2005. " Managerial Stock Ownership and the Maturity Structure of Corporate Debt." The Journal of Finance 60 : 2333 – 2350. doi: 10.1111/j.1540-6261.2005.00800.x. Diamond, D. W. 1991. " Debt Maturity Structure and Liquidity Risk." The Quarterly Journal of Economics 106 : 709 – 737. doi: 10.2307/2937924. Díaz-Díaz, N. L., P. J. García-Teruel, and P. Martínez-Solano. 2016. " Debt Maturity Structure in Private Firms: Does the Family Control Matter?." Journal of Corporate Finance 37 : 393 – 411. doi: 10.1016/j.jcorpfin.2016.01.016. Fama, E. F., and J. D. MacBeth. 1973. " Risk, Return and Equilibrium: Empirical Tests." Journal of Political Economy 81 : 607 – 636. doi: 10.1086/260061. Flannery, M. 1986. " Asymmetric Information and Risky Debt Maturity Choice." The Journal of Finance 41 : 18 – 38. doi: 10.1111/j.1540-6261.1986.tb04489.x. Guedes, J., and T. Opler. 1996. " The Determinants of the Maturity of Corporate Debt Issues." The Journal of Finance 51 : 1809 – 1833. doi: 10.1111/j.1540-6261.1996.tb05227.x. Huang, R., K. J. K. Tan, and R. W. Faff. 2016. " CEO Overconfidence and Corporate Debt Maturity." Journal of Corporate Finance 36 : 93 – 110. doi: 10.1016/j.jcorpfin.2015.10.009. Kane, A., A. J. Marcus, and R. L. McDonald. 1985. " Debt Policy and the Rate of Return Premium to Leverage." Journal of Financial and Quantitative Analysis 20 : 479 – 499. doi: 10.2307/2330763. Newey, W. K., and K. D. West. 1987. " A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix." Econometrica 55 : 703 – 708. doi: 10.2307/1913610. Orman, C., and B. Köksal. 2017. " Debt Maturity across Firm Types: Evidence from a Major Developing Economy." Emerging Markets Review 30 : 169 – 199. doi: 10.1016/j.ememar.2016.12.001. Scherr, F. C., and H. M. Hulburt. 2001. " The Debt Maturity Structure of Small Firms." Financial Management 30 : 85 – 111. http://www.jstor.org/stable/3666392. Scholes, M., and M. Wolfson. 1992. Taxes and Business Strategy: A Planning Approach. Englewood Cliffs, NJ : Prentice Hall. Smith, C. W., and J. B. Warner. 1979. " On Financial Contracting: An Analysis of Bond Covenants." Journal of Financial Economics 7 : 117 – 161. doi: 10.1016/0304-405X(79)90011-4. Stohs, M. H., and D. C. Mauer. 1996. " The Determinants of Corporate Debt Maturity Structure." The Journal of Business 69 : 279 – 312. http://www.jstor.org/stable/2353370. Vijayakumaran, S., and R. Vijayakumaran. 2019. " Debt Maturity and the Effects of Growth Opportunities and Liquidity Risk on Leverage: Evidence from Chinese Listed Companies." Journal of Asian Finance, Economics and Business 6 : 27 – 40. doi: 10.13106/jafeb.2019.vol6.no3.27. Prior studies have presented the following proxy variables for firm quality: abnormal earnings (Barclay and Smith [3]; Stohs and Mauer [22]; Datta, Iskandar-Datta, and Raman [9]) and Altman's Z-score (Brockman, Martin, and Unlu [7]; Huang, Tan, and Faff [15]). In this study, we use the latter as a proxy for firm quality because it is more directly associated with debt financing. Our empirical analysis focuses 'only' on the determinants that influence corporate debt maturity structure choice. However, the debt maturity choice is one of the decisions on corporate financial policy, including the cost of debt, the debt covenants, the choice between bank debt and public debt, and so on. Thus, further research should consider testing the joint determination of corporate financing decisions in the framework of simulation equations. We test the validity of the GMM estimation using the Arellano-Bond test for serial correlation and the Sargan-Hansen test of the overidentifying restrictions. The null hypothesis (H0) of the Arellano-Bond test is that there is no autocorrelation. The null hypothesis (H0) of the Sargan-Hansen test is that overidentifying restrictions are valid. In the GMM estimation, we use the lagged explanatory variables as instruments. The result from the Sargan-Hansen test indicates that our instruments are valid.

By Young Mok Choi and Kunsu Park

Reported by Author; Author